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Full time
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EF
Quantitative Analyst – Credit, Global Asset Manager, London - eFinancialCareers
Why This Role Stands Out
This hybrid Quantitative Analyst role offers a fantastic opportunity to develop cutting-edge credit models and drive impactful research within a highly reputable global asset manager. You'll thrive here if you possess strong quantitative skills, a passion for financial markets, and enjoy collaborating with diverse investment teams to shape the future of credit strategies. Apply now to elevate your career in a dynamic and rewarding environment.
Quick Overview
Seniority
Mid Senior
Employment type
Full Time
Work mode
Hybrid
Location
London, United Kingdom
Posted
Yesterday
CFA
Job Description
Responsibilities
- Develop quantitative credit models, including spread and default risk frameworks.
- Source, test and integrate new alpha signals across credit markets into research pipelines.
- Monitor model-generated trade ideas and disseminate outputs to investment teams.
- Produce rigorous standalone research with clear reports and presentations.
- Contribute thematic quantitative research and thought leadership across credit.
- Build and maintain the research platform and shared analytical codebase.
- Improve communication and visualisation of model outputs for portfolio teams.
- Uphold code quality, version control and systematic testing standards.
- Translate quantitative insights for Portfolio Managers, Credit Analysts, Traders and Investment Directors.
- Engage in cross-asset forums and collaborate across regions and disciplines.
- Support the internal and external profile of the quantitative research function.
- Discuss model methodology and research outputs with clients when required.
Requirements
- BSc/MSc in a quantitative discipline; strong statistics, econometrics and numerical methods.
- Proven track record building quantitative models in an investment or credit context.
- Strong Python (pandas, NumPy); SQL and Git advantageous.
- Advanced Excel; familiarity with Bloomberg or similar data platforms.
- Experience handling large, complex financial datasets.
- CFA or similar qualification a plus.
- Clear written and verbal communication of technical concepts.
- Collaborative and adaptable, comfortable working across functions and asset classes.
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